iSeg : A Keras 3 Library for Semantic Segmentation
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Updated
Jul 3, 2024 - Python
Deep learning is an AI function and a subset of machine learning, used for processing large amounts of complex data. Deep learning can automatically create algorithms based on data patterns.
iSeg : A Keras 3 Library for Semantic Segmentation
Graduation Project
アマデウス Memory storage and artificial intelligence system. Complex chatting AI bot for discord, also voice / music, streams trackers, WarCraft3 news, games trackers, various statistics/info grabbers.
Making large AI models cheaper, faster and more accessible
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Gather research papers, corresponding codes (if having), reading notes and any other related materials about Hot🔥🔥🔥 fields in Computer Vision based on Deep Learning.
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Linear Regression machine learning model
📚 Jupyter notebook tutorials for OpenVINO™
Open Source Computer Vision Library
A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
A small deep learning library that goes gigafast (not yet though).
Tensors and Dynamic neural networks in Python with strong GPU acceleration
codes for my deep learning experiments and projects
The YOLOv8-SORT-Human-Tracking repository demonstrates human tracking using YOLOv8 and the SORT algorithm, showcasing results of using YOLOv8 alone versus the combined method.
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
Enhancing automated essay scoring with advanced AI techniques using Keras-NLP and LSTM, aimed at improving educational outcomes through accurate assessment of student essays.
An Open Source Machine Learning Framework for Everyone
Software Development Kit (SDK) for the Intel® Geti™ platform for Computer Vision AI model training.
Efficient World Models with Context-Aware Tokenization. ICML 2024